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1.
Dennis Wagenaar Tiaravanni Hermawan Marc J. C. van den Homberg Jeroen C. J. H. Aerts Heidi Kreibich Hans de Moel Laurens M. Bouwer 《Risk analysis》2021,41(1):37-55
Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer. 相似文献
2.
In this article, we propose a novel approach for testing the equality of two log-normal populations using a computational approach test (CAT) that does not require explicit knowledge of the sampling distribution of the test statistic. Simulation studies demonstrate that the proposed approach can perform hypothesis testing with satisfying actual size even at small sample sizes. Overall, it is superior to other existing methods. Also, a CAT is proposed for testing about reliability of two log-normal populations when the means are the same. Simulations show that the actual size of this new approach is close to nominal level and better than the score test. At the end, the proposed methods are illustrated using two examples. 相似文献
3.
Cancho Vicente G. Macera Márcia A. C. Suzuki Adriano K. Louzada Francisco Zavaleta Katherine E. C. 《Lifetime data analysis》2020,26(2):221-244
Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there... 相似文献
4.
5.
de Jong Petra W. Caarls Kim de Valk Helga A. G. 《Population research and policy review》2022,41(2):671-694
Population Research and Policy Review - The welfare state can be perceived as a safety net which helps individuals adjust to situations of risk or transition. Starting from this idea of the welfare... 相似文献
6.
Sant’Anna Annibal Parracho de Freitas Siqueira Sadok Menna Barreto Márcia 《Social indicators research》2020,148(3):733-746
Social Indicators Research - This paper analyses the Human Development Index (HDI) time series from 2010 to 2017. An alternative index is studied, which combines the same components of the HDI by... 相似文献
7.
Thomas K. Kenemore 《Child and Adolescent Social Work Journal》2002,19(4):269-270
8.
9.
H. Küchenhoff 《Statistical Papers》1995,36(1):41-47
The simple logistic regression model with normal measurement error and normal regressor is shown to be identifiable without any extra information about the measurement error. The multiple logistic regression model with more than one regressor variable measured with error is not identifiable. If the covariance matrix of the measurement error is known up to a scalar factor, the model is identified. Further we discuss why in spite of the identifiability the models cannot be estimated in a reasonable way without extra information about the measurement error. 相似文献
10.
Nairobi ME Network African Evaluation Association Secretariat Rseau Nigrian de Suivi et Evaluation Cape Verde Evaluation Network Rseau Malagache de Suivi et Evaluation Comoros Evaluation Network Eritrean Evaluation Network Malawi ME Network Rseau National de Chercheurs et Evaluateurs de Burundi Rwanda Evaluation Network UNICEF Eastern Southern Africa Region ME Network 《Evaluation and program planning》2002,25(4)
A review of the US ‘program evaluation standards’ (PES), undertaken in a series of workshops and meetings of networks of evaluators in Africa, resulted in modifications to those standards. The result was presented to a plenary session of the Inaugural Conference of the African Evaluation Association in September 1999, attended by over 300 evaluators from 35 countries. The AfrEA Conference decided that a systematic effort should be made to produce a list of African evaluation guidelines, similar to the PES, and that this checklist should be reviewed by national evaluation associations and networks in Africa and field tested in several countries. Ten national and regional networks and associations suggested modifications to the text and endorsed the final version of the guidelines. 相似文献